Hybrid fuzzy multi-criteria decision making based multi cluster head dolphin swarm optimized IDS for VANET

Sharma, Sparsh; Kaul, Ajay

doi:10.1016/j.vehcom.2017.12.003

Cited by 81 publications

(41 citation statements)

References 24 publications

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“…Lately, due to the demonstrated enhancement resulted from the usage of AI in VANET protocols, a kind of intelligence was emerged to clustering in VANETs. Several clustering algorithms have used fuzzy logic [24], machine learning [25] and swarm intelligence [26, 27] to form more stable clusters. The common factor between different existing clustering algorithms is that all of them need to be optimised due to many configuration parameters which significantly affect the performance of the clustering.…”

Section: Related Workmentioning

confidence: 99%

Bio‐inspired metaheuristic framework for clustering optimisation in VANETs

Alsuhli

Fahmy²,

Khattab³

2020

IET intell. transp. syst

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Section: Related Workmentioning

confidence: 99%

Bio‐inspired metaheuristic framework for clustering optimisation in VANETs

Alsuhli

Fahmy²,

Khattab³

2020

IET intell. transp. syst

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“…The proposed method improved the performance and working of the network. Further, a hybrid fuzzy-based multi-criteria decision-making method was proposed for the selection of cluster head from available nodes [31]. A dynamic mobility and stability based clustering method were proposed for the VANET.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…In the Figure 6, y-axis label denotes the probability of detection only (not to confuse with the notation used in Equation (32). The threshold value to decide the absence or presence of PU is calculated from Equation (31). P f (0, 1) is used to check various P d and P * d .…”

Section: Proposed Workmentioning

confidence: 99%

Expansion of Cluster Head Stability Using Fuzzy in Cognitive Radio CR-VANET

et al. 2019

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The Vehicular Ad Hoc Network (VANET) plays a vital role in the development of smart cities, especially in ensuring vehicles' safety on roads. However, VANET wireless-based networks face some challenges such as security, stability, communication, and reliability. To resolve these issues, we propose a fuzzy cluster head selection scheme in Cognitive Radio (CR) VANET, which uses the CR technology for the spectrum sensing algorithm. In this technology, the free spectrums of the primary user are utilized by secondary users without any correlation. Moreover, we have considered some input parameters such as vehicles' average velocity, distance, network connectivity level, lane weight and trustworthiness for the fuzzy system based CR VANET in this research. The selected cluster head provides stability and reliability to the cluster compared to the state of art techniques. Extensive experiments were conducted in order to evaluate the effectiveness of the proposed approach. However, simulation results authenticate more stable and secure cluster formation using the proposed fuzzy logic based CR VANET.

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“…The authors in another study [24] proposed a review of the intrusion detection system (IDS). A survey of IDS, based upon DoS attacks, has been provided with the examination and comparison of every technique, with advantages and disadvantages.…”

Section: Securing Vanet-centralized Architecture Based On Dos Attacksmentioning

confidence: 99%

Real-Time Detection of DoS Attacks in IEEE 802.11p Using Fog Computing for a Secure Intelligent Vehicular Network

Erskine

Elleithy

2019

Electronics

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The vehicular ad hoc network (VANET) is a method through which Intelligent Transportation Systems (ITS) have become important for the benefit of daily life. Real-time detection of all forms of attacks, including hybrid DoS attacks in IEEE 802.11p, has become an urgent issue for VANET. This is due to sporadic real-time exchange of safety and road emergency message delivery in VANET. Sporadic communication in VANET has the tendency to generate an enormous amount of messages. This leads to overutilization of the road side unit (RSU) or the central processing unit (CPU) for computation. Therefore, efficient storage and intelligent VANET infrastructure architecture (VIA), which includes trustworthiness, are required. Vehicular Cloud and Fog Computing (VFC) play an important role in efficient storage, computation, and communication needs for VANET. This research utilizes VFC integration with hybrid optimization algorithms (OAs), which also possess swarm intelligence, including Cuckoo/CSA Artificial Bee Colony (ABC) and Firefly/Genetic Algorithm (GA), to provide real-time detection of DoS attacks in IEEE 802.11p, using VFC for a secure intelligent vehicular network. Vehicles move ar a certain speed and the data is transmitted at 30 Mbps. Firefly Feed forward back propagation neural network (FFBPNN) is used as a classifier to distinguish between the attacked vehicles and the genuine vehicles. The proposed scheme is compared with Cuckoo/CSA ABC and Firefly GA by considering jitter, throughput, and prediction accuracy.

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Hybrid fuzzy multi-criteria decision making based multi cluster head dolphin swarm optimized IDS for VANET

Cited by 81 publications

References 24 publications

Bio‐inspired metaheuristic framework for clustering optimisation in VANETs

Bio‐inspired metaheuristic framework for clustering optimisation in VANETs

Expansion of Cluster Head Stability Using Fuzzy in Cognitive Radio CR-VANET

Real-Time Detection of DoS Attacks in IEEE 802.11p Using Fog Computing for a Secure Intelligent Vehicular Network

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